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covid_NAs <- covid %>% 
  group_by(location) %>% 
  summarise_all(funs(sum(is.na(.)))) %>% 
  pivot_longer(cols = -location, names_to = "Variable", values_to = "NAs") %>% 
  mutate(Percent = round(NAs / nrow(covid) * 100 ,2)) %>% 
  arrange(-NAs)
Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
covid_NAs %>% 
  group_by(location) %>% 
  summarise(total_pct_na = sum(Percent)) %>% 
  arrange(total_pct_na) %>% 
  datatable(filter = 'top')
#Helper function for filtering data
my_data <- function(country_covid_filter, country_gisaid_filter){
  data <- covid %>% 
    filter(location == country_covid_filter)
  gisaid_data <- gisaid_variants %>% 
    filter(Country == country_gisaid_filter)
  data <- left_join(data, gisaid_data, by = "date")
  data
}

us <- my_data("United States", "USA")
variants_plot <- ggplot(data = us, aes(x = date)) +
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), position = "dodge", show.legend = FALSE) +
  theme_minimal()


cases_plot <-
  ggplot(data = us, aes(x = date)) +
  geom_line(aes(y = new_cases_per_million), show.legend = FALSE) +
  geom_line(aes(y = new_deaths_per_million)) + 
  theme_minimal()


deaths_plot <- ggplot(data = us, aes(x = date)) +
  geom_line(aes(y = new_deaths_per_million)) + 
  theme_minimal()


vaccinations_plot <- ggplot(us, aes(x = date)) +
  geom_line(aes(y = new_vaccinations_smoothed_per_million)) +
  theme_minimal()


variants_cases_plot <- ggplot(data = us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = new_cases_smoothed_per_million / 40)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~.*40, name = "New Cases Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs New Cases Per Million")  +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")


variants_deaths_plot <- ggplot(data = us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant),show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = new_deaths_smoothed_per_million*5)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~./5, name = "New Deaths Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs New Deaths Per Million") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")


cases_vaccinations_plot <- ggplot(data = us, aes(x = date)) +
  geom_line(aes(y = new_cases_smoothed_per_million), show.legend = FALSE) +
  geom_line(aes(y = people_vaccinated_per_hundred*50)) + 
  scale_y_continuous("New Cases Per Million", sec.axis=sec_axis(~./50, name = "People Vaccinated Per Hundred")) + 
  theme_minimal() + 
  labs(title = "New Cases Per Million vs. People Vaccinated Per Hundred")

deaths_vaccinations_plot <- ggplot(us, aes(x = date)) +
  geom_line(aes(y = new_deaths_per_million), show.legend = FALSE) +
  geom_line(aes(y = people_fully_vaccinated_per_hundred/7)) + 
  scale_y_continuous("New Deaths Per Million", sec.axis=sec_axis(~.*7, name = "People Vaccinated Per Hundred")) + 
  theme_minimal() + 
  labs(title = "New Deaths Per Million vs. People Fully Vaccinated Per Hundred")


variants_hospitalizations_plot <- ggplot(us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = weekly_hosp_admissions_per_million / 5)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~.*5, name = "Hospitalizations Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs Hospitalizations Per Million") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")

variants_vaccinations_plot <- ggplot(us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = people_fully_vaccinated_per_hundred)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~., name = "People Vaccinated Per Hundred")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs People Fully Vaccinated") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")

variants_vaccinations_plot
vaccinations_plot
Warning: Removed 404 row(s) containing missing values (geom_path).

variants_cases_plot <- ggplot(data = us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = new_cases_smoothed_per_million / 40)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~.*40, name = "New Cases Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs New Cases Per Million") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")

variants_cases_plot
Warning: Width not defined. Set with `position_dodge(width = ?)`
Warning in max(ids, na.rm = TRUE) :
  no non-missing arguments to max; returning -Inf
Warning: Removed 7 row(s) containing missing values (geom_path).

variants_hospitalizations_plot
Warning: Width not defined. Set with `position_dodge(width = ?)`
Warning in max(ids, na.rm = TRUE) :
  no non-missing arguments to max; returning -Inf
Warning: Removed 206 row(s) containing missing values (geom_path).

variants_deaths_plot
Warning: Width not defined. Set with `position_dodge(width = ?)`
Warning in max(ids, na.rm = TRUE) :
  no non-missing arguments to max; returning -Inf
Warning: Removed 45 row(s) containing missing values (geom_path).

Time Series Analysis

us %>% 
  plot_time_series(date, new_cases_smoothed_per_million)
us %>% 
  plot_acf_diagnostics(date, new_cases_smoothed_per_million, .show_white_noise_bars = T) 
gisaid_variants %>% 
  filter(Country == "USA", variant %in% c("VOC Omicron GRA (B.1.1.529+BA.*) ", "VOC Delta GK (B.1.617.2+AY.*) ", "VOC Alpha GRY (B.1.1.7+Q.*) ")) %>% 
  plot_time_series(date, perc_sequences, .facet_vars=variant, .legend_show = FALSE)
gisaid_variants %>% 
  filter(Country == "USA", variant %in% c("VOC Omicron GRA (B.1.1.529+BA.*) ", "VOC Delta GK (B.1.617.2+AY.*) ", "VOC Alpha GRY (B.1.1.7+Q.*) ")) %>% 
  group_by(variant) %>% 
  plot_acf_diagnostics(date, perc_sequences, .show_white_noise_bars = T) 
Max lag exceeds data available. Using max lag: 73
Max lag exceeds data available. Using max lag: 80
Max lag exceeds data available. Using max lag: 22
library("sf")
Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE
library("rnaturalearth")
library("rnaturalearthdata")

world <- ne_countries(scale = "medium", returnclass = "sf")
class(world)
[1] "sf"         "data.frame"
unique(gisaid$Country)
  [1] "Afghanistan"                      "Albania"                          "Algeria"                          "American Samoa"                  
  [5] "Andorra"                          "Angola"                           "Anguilla"                         "Antigua and Barbuda"             
  [9] "Argentina"                        "Armenia"                          "Aruba"                            "Australia"                       
 [13] "Austria"                          "Azerbaijan"                       "Bahrain"                          "Bangladesh"                      
 [17] "Barbados"                         "Belarus"                          "Belgium"                          "Belize"                          
 [21] "Benin"                            "Bermuda"                          "Bolivia"                          "Bonaire"                         
 [25] "Bosnia and Herzegovina"           "Botswana"                         "Brazil"                           "British Virgin Islands"          
 [29] "Brunei"                           "Bulgaria"                         "Burkina Faso"                     "Burundi"                         
 [33] "Cabo Verde"                       "Cambodia"                         "Cameroon"                         "Canada"                          
 [37] "Canary Islands"                   "Cayman Islands"                   "Central African Republic"         "Chad"                            
 [41] "Chile"                            "China"                            "Colombia"                         "Comoros"                         
 [45] "Costa Rica"                       "Cote d'Ivoire"                    "Crimea"                           "Croatia"                         
 [49] "Cuba"                             "Curacao"                          "Cyprus"                           "Czech Republic"                  
 [53] "Democratic Republic of the Congo" "Denmark"                          "Djibouti"                         "Dominica"                        
 [57] "Dominican Republic"               "Ecuador"                          "Egypt"                            "El Salvador"                     
 [61] "Equatorial Guinea"                "Estonia"                          "Eswatini"                         "Ethiopia"                        
 [65] "Faroe Islands"                    "Fiji"                             "Finland"                          "France"                          
 [69] "French Guiana"                    "French Polynesia"                 "Gabon"                            "Gambia"                          
 [73] "Georgia"                          "Germany"                          "Ghana"                            "Gibraltar"                       
 [77] "Greece"                           "Grenada"                          "Guadeloupe"                       "Guam"                            
 [81] "Guatemala"                        "Guinea"                           "Guinea-Bissau"                    "Guyana"                          
 [85] "Haiti"                            "Honduras"                         "Hong Kong"                        "Hungary"                         
 [89] "Iceland"                          "India"                            "Indonesia"                        "Iran"                            
 [93] "Iraq"                             "Ireland"                          "Israel"                           "Italy"                           
 [97] "Jamaica"                          "Japan"                            "Jordan"                           "Kazakhstan"                      
[101] "Kenya"                            "Kosovo"                           "Kuwait"                           "Kyrgyzstan"                      
[105] "Laos"                             "Latvia"                           "Lebanon"                          "Lesotho"                         
[109] "Liberia"                          "Libya"                            "Liechtenstein"                    "Lithuania"                       
[113] "Luxembourg"                       "Madagascar"                       "Malawi"                           "Malaysia"                        
[117] "Maldives"                         "Mali"                             "Malta"                            "Martinique"                      
[121] "Mauritania"                       "Mauritius"                        "Mayotte"                          "Mexico"                          
[125] "Moldova"                          "Monaco"                           "Mongolia"                         "Montenegro"                      
[129] "Montserrat"                       "Morocco"                          "Mozambique"                       "Myanmar"                         
[133] "Namibia"                          "Nepal"                            "Netherlands"                      "New Caledonia"                   
[137] "New Zealand"                      "Nicaragua"                        "Niger"                            "Nigeria"                         
[141] "North Macedonia"                  "Northern Mariana Islands"         "Norway"                           "Oman"                            
[145] "Pakistan"                         "Palau"                            "Palestine"                        "Panama"                          
[149] "Papua New Guinea"                 "Paraguay"                         "Peru"                             "Philippines"                     
[153] "Poland"                           "Portugal"                         "Puerto Rico"                      "Qatar"                           
[157] "Republic of the Congo"            "Reunion"                          "Romania"                          "Russia"                          
[161] "Rwanda"                           "Saint Barthelemy"                 "Saint Kitts and Nevis"            "Saint Lucia"                     
[165] "Saint Martin"                     "Saint Vincent and the Grenadines" "Sao Tome and Principe"            "Saudi Arabia"                    
[169] "Senegal"                          "Serbia"                           "Seychelles"                       "Sierra Leone"                    
[173] "Singapore"                        "Sint Eustatius"                   "Sint Maarten"                     "Slovakia"                        
[177] "Slovenia"                         "Solomon Islands"                  "Somalia"                          "South Africa"                    
[181] "South Korea"                      "South Sudan"                      "Spain"                            "Sri Lanka"                       
[185] "Sudan"                            "Suriname"                         "Sweden"                           "Switzerland"                     
[189] "Syria"                            "Taiwan"                           "Tanzania"                         "Thailand"                        
[193] "The Bahamas"                      "Timor-Leste"                      "Togo"                             "Trinidad and Tobago"             
[197] "Tunisia"                          "Turkey"                           "Turks and Caicos Islands"         "U.S. Virgin Islands"             
[201] "USA"                              "Uganda"                           "Ukraine"                          "United Arab Emirates"            
[205] "United Kingdom"                   "Uruguay"                          "Uzbekistan"                       "Vanuatu"                         
[209] "Venezuela"                        "Vietnam"                          "Wallis and Futuna Islands"        "Zambia"                          
[213] "Zimbabwe"                        
unique(covid$location)
  [1] "Afghanistan"                      "Africa"                           "Albania"                          "Algeria"                         
  [5] "Andorra"                          "Angola"                           "Anguilla"                         "Antigua and Barbuda"             
  [9] "Argentina"                        "Armenia"                          "Aruba"                            "Asia"                            
 [13] "Australia"                        "Austria"                          "Azerbaijan"                       "Bahamas"                         
 [17] "Bahrain"                          "Bangladesh"                       "Barbados"                         "Belarus"                         
 [21] "Belgium"                          "Belize"                           "Benin"                            "Bermuda"                         
 [25] "Bhutan"                           "Bolivia"                          "Bonaire Sint Eustatius and Saba"  "Bosnia and Herzegovina"          
 [29] "Botswana"                         "Brazil"                           "British Virgin Islands"           "Brunei"                          
 [33] "Bulgaria"                         "Burkina Faso"                     "Burundi"                          "Cambodia"                        
 [37] "Cameroon"                         "Canada"                           "Cape Verde"                       "Cayman Islands"                  
 [41] "Central African Republic"         "Chad"                             "Chile"                            "China"                           
 [45] "Colombia"                         "Comoros"                          "Congo"                            "Cook Islands"                    
 [49] "Costa Rica"                       "Cote d'Ivoire"                    "Croatia"                          "Cuba"                            
 [53] "Curacao"                          "Cyprus"                           "Czechia"                          "Democratic Republic of Congo"    
 [57] "Denmark"                          "Djibouti"                         "Dominica"                         "Dominican Republic"              
 [61] "Ecuador"                          "Egypt"                            "El Salvador"                      "Equatorial Guinea"               
 [65] "Eritrea"                          "Estonia"                          "Eswatini"                         "Ethiopia"                        
 [69] "Europe"                           "European Union"                   "Faeroe Islands"                   "Falkland Islands"                
 [73] "Fiji"                             "Finland"                          "France"                           "French Polynesia"                
 [77] "Gabon"                            "Gambia"                           "Georgia"                          "Germany"                         
 [81] "Ghana"                            "Gibraltar"                        "Greece"                           "Greenland"                       
 [85] "Grenada"                          "Guam"                             "Guatemala"                        "Guernsey"                        
 [89] "Guinea"                           "Guinea-Bissau"                    "Guyana"                           "Haiti"                           
 [93] "High income"                      "Honduras"                         "Hong Kong"                        "Hungary"                         
 [97] "Iceland"                          "India"                            "Indonesia"                        "International"                   
[101] "Iran"                             "Iraq"                             "Ireland"                          "Isle of Man"                     
[105] "Israel"                           "Italy"                            "Jamaica"                          "Japan"                           
[109] "Jersey"                           "Jordan"                           "Kazakhstan"                       "Kenya"                           
[113] "Kiribati"                         "Kosovo"                           "Kuwait"                           "Kyrgyzstan"                      
[117] "Laos"                             "Latvia"                           "Lebanon"                          "Lesotho"                         
[121] "Liberia"                          "Libya"                            "Liechtenstein"                    "Lithuania"                       
[125] "Low income"                       "Lower middle income"              "Luxembourg"                       "Macao"                           
[129] "Madagascar"                       "Malawi"                           "Malaysia"                         "Maldives"                        
[133] "Mali"                             "Malta"                            "Marshall Islands"                 "Mauritania"                      
[137] "Mauritius"                        "Mexico"                           "Micronesia (country)"             "Moldova"                         
[141] "Monaco"                           "Mongolia"                         "Montenegro"                       "Montserrat"                      
[145] "Morocco"                          "Mozambique"                       "Myanmar"                          "Namibia"                         
[149] "Nauru"                            "Nepal"                            "Netherlands"                      "New Caledonia"                   
[153] "New Zealand"                      "Nicaragua"                        "Niger"                            "Nigeria"                         
[157] "Niue"                             "North America"                    "North Macedonia"                  "Northern Cyprus"                 
[161] "Northern Mariana Islands"         "Norway"                           "Oceania"                          "Oman"                            
[165] "Pakistan"                         "Palau"                            "Palestine"                        "Panama"                          
[169] "Papua New Guinea"                 "Paraguay"                         "Peru"                             "Philippines"                     
[173] "Pitcairn"                         "Poland"                           "Portugal"                         "Puerto Rico"                     
[177] "Qatar"                            "Romania"                          "Russia"                           "Rwanda"                          
[181] "Saint Helena"                     "Saint Kitts and Nevis"            "Saint Lucia"                      "Saint Pierre and Miquelon"       
[185] "Saint Vincent and the Grenadines" "Samoa"                            "San Marino"                       "Sao Tome and Principe"           
[189] "Saudi Arabia"                     "Senegal"                          "Serbia"                           "Seychelles"                      
[193] "Sierra Leone"                     "Singapore"                        "Sint Maarten (Dutch part)"        "Slovakia"                        
[197] "Slovenia"                         "Solomon Islands"                  "Somalia"                          "South Africa"                    
[201] "South America"                    "South Korea"                      "South Sudan"                      "Spain"                           
[205] "Sri Lanka"                        "Sudan"                            "Suriname"                         "Sweden"                          
[209] "Switzerland"                      "Syria"                            "Taiwan"                           "Tajikistan"                      
[213] "Tanzania"                         "Thailand"                         "Timor"                            "Togo"                            
[217] "Tokelau"                          "Tonga"                            "Trinidad and Tobago"              "Tunisia"                         
[221] "Turkey"                           "Turkmenistan"                     "Turks and Caicos Islands"         "Tuvalu"                          
[225] "Uganda"                           "Ukraine"                          "United Arab Emirates"             "United Kingdom"                  
[229] "United States"                    "United States Virgin Islands"     "Upper middle income"              "Uruguay"                         
[233] "Uzbekistan"                       "Vanuatu"                          "Vatican"                          "Venezuela"                       
[237] "Vietnam"                          "Wallis and Futuna"                "World"                            "Yemen"                           
[241] "Zambia"                           "Zimbabwe"                        
world_variants <- gisaid %>% 
  group_by(Country) %>% 
  mutate(Country = case_when(
  Country == "USA" ~ "United States",
  TRUE ~ as.character(Country)
  )) %>% 
  summarise(most_recent_date = date[n()], 
            prevalent_variant = variant[date == date[n()] & Type == "Variant" & perc_sequences == max(perc_sequences)]) %>% 
            #prevalent_lineage = variant[date == date[n()] & Type == "Lineage" & perc_sequences == max(perc_sequences)]) %>% 
  rename(location = Country)
`summarise()` has grouped output by 'Country'. You can override using the `.groups` argument.
world_variants <- left_join(world_variants, covid[, c("location", "iso_code")], by = "location", all.x = TRUE) %>% 
  rename(gu_a3 = iso_code)

world_variants_map <- left_join(world, world_variants, by = "gu_a3", all.x = TRUE)
p <- ggplot(data = world_variants_map, aes(fill = prevalent_variant)) + 
  geom_sf(show.legend = FALSE) + 
  xlab("Longitude") + 
  ylab("Latitude") + 
  theme(panel.grid.major = element_line(color = gray(.5), linetype = "dashed", size = 0.5), panel.background = element_rect(fill = "aliceblue")) 
#p
---
title: "COVID-19 Variants Analysis"
output: html_notebook
---

```{r, include = FALSE}
#knitr::opts_chunk$set(echo = FALSE, warning = FALSE)
```

```{r, echo = FALSE, message=FALSE, warning=FALSE, comment=FALSE}
library(tidyverse)
library(scales)
library(plotly)
library(DT)
library(data.table)
library(gtable)
library(plotly)
library(lubridate)
library(vtable)
library(rjson)
library(timetk)
library(Rssa)
```

```{r, echo = FALSE, warning=FALSE}
covid <- read_csv("data/owid-covid-data.csv",show_col_types = FALSE)
#variants <- read_csv("data/covid-variants.csv",show_col_types = FALSE)

gisaid <- as.data.frame(fread("data/gisaid_variants_statistics.tsv")) %>% 
  rename(date = `Week prior to`,
         count = `Submission Count`,
         perc_sequences = `% per Country and Week`,
         total = `Total per Country and Week`,
         variant = Value) %>% 
  mutate(date = ymd(date),
         perc_sequences = round(count / total * 100, 3))# %>% 
#  separate(variant, into = c("variant", "origin"), sep = c("first detected in "))

gisaid_variants <- gisaid %>% 
  filter(Type == "Variant") %>%
  separate(variant, c("variant", "origin"), sep = "first detected in ") %>% 
  select(-Type)
```

```{r}
covid_NAs <- covid %>% 
  group_by(location) %>% 
  summarise_all(funs(sum(is.na(.)))) %>% 
  pivot_longer(cols = -location, names_to = "Variable", values_to = "NAs") %>% 
  mutate(Percent = round(NAs / nrow(covid) * 100 ,2)) %>% 
  arrange(-NAs)
```

```{r}
covid_NAs %>% 
  group_by(location) %>% 
  summarise(total_pct_na = sum(Percent)) %>% 
  arrange(total_pct_na) %>% 
  datatable(filter = 'top')
```

```{r}
#Helper function for filtering data
my_data <- function(country_covid_filter, country_gisaid_filter){
  data <- covid %>% 
    filter(location == country_covid_filter)
  gisaid_data <- gisaid_variants %>% 
    filter(Country == country_gisaid_filter)
  data <- left_join(data, gisaid_data, by = "date")
  data
}

us <- my_data("United States", "USA")
```


```{r "US Plots"}
variants_plot <- ggplot(data = us, aes(x = date)) +
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), position = "dodge", show.legend = FALSE) +
  theme_minimal()


cases_plot <-
  ggplot(data = us, aes(x = date)) +
  geom_line(aes(y = new_cases_per_million), show.legend = FALSE) +
  geom_line(aes(y = new_deaths_per_million)) + 
  theme_minimal()


deaths_plot <- ggplot(data = us, aes(x = date)) +
  geom_line(aes(y = new_deaths_per_million)) + 
  theme_minimal()


vaccinations_plot <- ggplot(us, aes(x = date)) +
  geom_line(aes(y = new_vaccinations_smoothed_per_million)) +
  theme_minimal()


variants_cases_plot <- ggplot(data = us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = new_cases_smoothed_per_million / 40)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~.*40, name = "New Cases Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs New Cases Per Million")  +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")


variants_deaths_plot <- ggplot(data = us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant),show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = new_deaths_smoothed_per_million*5)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~./5, name = "New Deaths Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs New Deaths Per Million") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")


cases_vaccinations_plot <- ggplot(data = us, aes(x = date)) +
  geom_line(aes(y = new_cases_smoothed_per_million), show.legend = FALSE) +
  geom_line(aes(y = people_vaccinated_per_hundred*50)) + 
  scale_y_continuous("New Cases Per Million", sec.axis=sec_axis(~./50, name = "People Vaccinated Per Hundred")) + 
  theme_minimal() + 
  labs(title = "New Cases Per Million vs. People Vaccinated Per Hundred")

deaths_vaccinations_plot <- ggplot(us, aes(x = date)) +
  geom_line(aes(y = new_deaths_per_million), show.legend = FALSE) +
  geom_line(aes(y = people_fully_vaccinated_per_hundred/7)) + 
  scale_y_continuous("New Deaths Per Million", sec.axis=sec_axis(~.*7, name = "People Vaccinated Per Hundred")) + 
  theme_minimal() + 
  labs(title = "New Deaths Per Million vs. People Fully Vaccinated Per Hundred")


variants_hospitalizations_plot <- ggplot(us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = weekly_hosp_admissions_per_million / 5)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~.*5, name = "Hospitalizations Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs Hospitalizations Per Million") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")

variants_vaccinations_plot <- ggplot(us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = people_fully_vaccinated_per_hundred)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~., name = "People Vaccinated Per Hundred")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs People Fully Vaccinated") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")

variants_vaccinations_plot
```

```{r}
vaccinations_plot
```

```{r}
variants_cases_plot <- ggplot(data = us, aes(x = date)) + 
  geom_area(aes(y = perc_sequences, color = variant, fill = variant), show.legend =FALSE, alpha = 0.5, position = "dodge") + 
  geom_line(aes(y = new_cases_smoothed_per_million / 40)) + 
  scale_y_continuous("Percent of Sequences", sec.axis=sec_axis(~.*40, name = "New Cases Per Million")) + 
  theme_minimal() + 
  labs(title = "Proportion of Covid Variants vs New Cases Per Million") +
  annotate(geom="label", x=ymd(20200701), y=75, label="Alpha/Other") +
  annotate(geom="label", x=ymd(20210901), y=75, label="Delta") +
  annotate(geom="label", x=ymd(20220201), y=75, label="Omicron")

variants_cases_plot
```

```{r}
variants_hospitalizations_plot
```

```{r}
variants_deaths_plot
```


# Time Series Analysis

```{r}
us %>% 
  plot_time_series(date, new_cases_smoothed_per_million)
```

```{r}
us %>% 
  plot_acf_diagnostics(date, new_cases_smoothed_per_million, .show_white_noise_bars = T) 
```


```{r}
gisaid_variants %>% 
  filter(Country == "USA", variant %in% c("VOC Omicron GRA (B.1.1.529+BA.*) ", "VOC Delta GK (B.1.617.2+AY.*) ", "VOC Alpha GRY (B.1.1.7+Q.*) ")) %>% 
  plot_time_series(date, perc_sequences, .facet_vars=variant, .legend_show = FALSE)
```

```{r}
gisaid_variants %>% 
  filter(Country == "USA", variant %in% c("VOC Omicron GRA (B.1.1.529+BA.*) ", "VOC Delta GK (B.1.617.2+AY.*) ", "VOC Alpha GRY (B.1.1.7+Q.*) ")) %>% 
  group_by(variant) %>% 
  plot_acf_diagnostics(date, perc_sequences, .show_white_noise_bars = T) 
```

```{r}
library("sf")
library("rnaturalearth")
library("rnaturalearthdata")

world <- ne_countries(scale = "medium", returnclass = "sf")
class(world)
```

```{r}
unique(gisaid$Country)
```

```{r}
unique(covid$location)
```


```{r}
world_variants <- gisaid %>% 
  group_by(Country) %>% 
  mutate(Country = case_when(
  Country == "USA" ~ "United States",
  TRUE ~ as.character(Country)
  )) %>% 
  summarise(most_recent_date = date[n()], 
            prevalent_variant = variant[date == date[n()] & Type == "Variant" & perc_sequences == max(perc_sequences)]) %>% 
            #prevalent_lineage = variant[date == date[n()] & Type == "Lineage" & perc_sequences == max(perc_sequences)]) %>% 
  rename(location = Country)

world_variants <- left_join(world_variants, covid[, c("location", "iso_code")], by = "location", all.x = TRUE) %>% 
  rename(gu_a3 = iso_code)

world_variants_map <- left_join(world, world_variants, by = "gu_a3", all.x = TRUE)
```

```{r}
p <- ggplot(data = world_variants_map, aes(fill = prevalent_variant)) + 
  geom_sf(show.legend = FALSE) + 
  xlab("Longitude") + 
  ylab("Latitude") + 
  theme(panels.grid.major = element_line(color = gray(.5), linetype = "dashed", size = 0.5), panel.background = element_rect(fill = "aliceblue")) 
```

```{r}
#p
```


